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Stocks Daily Splits
Every stock split and reverse split of a US-listed name, with its ratio and the factor applied to earlier prices.
Shape measured at the next refresh of dataset_cards/facts.json. Refreshed monthly.
Why It Matters
Stocks-Daily-Price is back-adjusted, so a split leaves no trace in it: the price series shows nothing on the day. That hides the event the split papers study -- the announcement drift, the post-split change in liquidity and in the shareholder base, and the reverse split as a distress signal. This table is the event. A row with split_from 1 and split_to 4 is a 4-for-1 split; split_from 10 and split_to 1 is a 1-for-10 reverse split.
Load It
pip install --upgrade pwb-toolbox
from pwb_toolbox import datasets as pwb_ds
df = pwb_ds.load_dataset("Stocks-Daily-Splits", symbols=["AAPL"])
print(df.iloc[-1, :])
Columns
| Column Name | Description |
|---|---|
| symbol | Stock ticker. |
| date | Execution date: the first session the shares trade on the new basis. |
| split_from | Shares held before, per split_to shares after. |
| split_to | Shares held after, per split_from shares before. |
| adjustment_type | The vendor's classification of the action (forward, reverse, ...). |
| historical_adjustment_factor | Factor to multiply prices before date by, to put them on the post-split basis. |
Access
Browsing the card and the schema is open to anyone. Downloading the files needs an approved request, tied to a subscription: what each plan includes. The same subscription covers every dataset in this organisation and the strategy catalogue.
Elsewhere
- The strategy catalogue
pwb-toolbox, the loader used in the snippet above- Every dataset in this organisation
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